




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


lowerCamelCase							:						Any					       =  logging.get_logger(__name__)

lowerCamelCase							:						Optional[int]					       =  {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowerCamelCase							:						List[Any]					       =  {
    "tokenizer_file": {
        "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json",
    },
}

lowerCamelCase							:						Dict					       =  {
    "gpt-neox-20b": 2_048,
}







class     A(		UpperCamelCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		VOCAB_FILES_NAMES
 UpperCamelCase		  =		PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase		  =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase		  =		['''input_ids''', '''attention_mask''']

 def __init__(							self   :    List[str]		,				A_   :    List[Any]=None		,				A_   :    Tuple=None		,				A_   :    Union[str, Any]=None		,				A_   :    Any="<|endoftext|>"		,				A_   :    str="<|endoftext|>"		,				A_   :    int="<|endoftext|>"		,				A_   :    Optional[Any]=False		,				**A_   :    List[Any]		,				)  ->      int:

       """simple docstring"""






       super().__init__(
           A_		,				A_		,				tokenizer_file=A_		,				unk_token=A_		,				bos_token=A_		,				eos_token=A_		,				add_prefix_space=A_		,				**A_		,				)

       lowerCamelCase_							=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
       if pre_tok_state.get('add_prefix_space'		,				A_    ) != add_prefix_space:
             lowerCamelCase_							=       getattr(A_		,				pre_tok_state.pop('type'    )    )
             lowerCamelCase_							=       add_prefix_space
             lowerCamelCase_							=       pre_tok_class(**A_    )

       lowerCamelCase_							=       add_prefix_space

 def 							a__					(							self   :    Optional[int]		,				A_   :    str		,				A_   :    Optional[str] = None    )  ->      Tuple[str]:

       """simple docstring"""






       lowerCamelCase_							=       self._tokenizer.model.save(A_		,				name=A_    )
       return tuple(A_    )





 def 							a__					(							self   :    List[str]		,				A_   :    "Conversation"    )  ->      List[int]:

       """simple docstring"""






       lowerCamelCase_							=       []
       for is_user, text in conversation.iter_texts():
             input_ids.extend(self.encode(A_		,				add_special_tokens=A_    ) + [self.eos_token_id]    )

       if len(A_    ) > self.model_max_length:
             lowerCamelCase_							=       input_ids[-self.model_max_length :]
       return input_ids








'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
     from PIL import Image

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """naver-clova-ix/donut-base-finetuned-docvqa"""
    lowercase__					=       (
        """This is a tool that answers a question about an document (pdf). It takes an input named `document` which """
        """should be the document containing the information, as well as a `question` that is the question about the """
        """document. It returns a text that contains the answer to the question."""
    )
    lowercase__					=       """document_qa"""
    lowercase__					=       AutoProcessor
    lowercase__					=       VisionEncoderDecoderModel

    lowercase__					=       ["""image""", """text"""]
    lowercase__					=       ["""text"""]







    def __init__( self					:    str		,     *__snake_case					:    Any		,     **__snake_case					:    List[Any]      ):
      if not is_vision_available():
        raise ValueError('Pillow must be installed to use the DocumentQuestionAnsweringTool.'      )

      super().__init__(*__snake_case		,     **__snake_case      )







    def      lowercase_			( self					:    Union[str, Any]		,     __snake_case					:    "Image"		,     __snake_case					:    str      ):
      a		:   Union[str, Any]	  =	'<s_docvqa><s_question>{user_input}</s_question><s_answer>'
      a		:   Any	  =	task_prompt.replace('{user_input}'		,     __snake_case      )
      a		:   Dict	  =	self.pre_processor.tokenizer(
          __snake_case		,     add_special_tokens=__snake_case		,     return_tensors='pt'      ).input_ids
      a		:   Tuple	  =	self.pre_processor(__snake_case		,     return_tensors='pt'      ).pixel_values

      return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}







    def      lowercase_			( self					:    Any		,     __snake_case					:    int      ):
      return self.model.generate(
          inputs['pixel_values'].to(self.device      )		,     decoder_input_ids=inputs['decoder_input_ids'].to(self.device      )		,     max_length=self.model.decoder.config.max_position_embeddings		,     early_stopping=__snake_case		,     pad_token_id=self.pre_processor.tokenizer.pad_token_id		,     eos_token_id=self.pre_processor.tokenizer.eos_token_id		,     use_cache=__snake_case		,     num_beams=1		,     bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]		,     return_dict_in_generate=__snake_case		,     ).sequences







    def      lowercase_			( self					:    List[str]		,     __snake_case					:    Tuple      ):
      a		:   str	  =	self.pre_processor.batch_decode(__snake_case      )[0]
      a		:   str	  =	sequence.replace(self.pre_processor.tokenizer.eos_token		,     ''      )
      a		:   Union[str, Any]	  =	sequence.replace(self.pre_processor.tokenizer.pad_token		,     ''      )
      a		:   List[str]	  =	re.sub(r'<.*?>'		,     ''		,     __snake_case		,     count=1      ).strip()  # remove first task start token
      a		:   Dict	  =	self.pre_processor.tokenajson(__snake_case      )

      return sequence["answer"]